Finger-Vein Recognition Based on the Score Level Moment Invariants Fusion

This paper expresses an algorithm of finger-vein recognition based on the score level moment invariants fusion. With regard the both characteristics of low contrast and intensity inhomogeneity in the infrared vein images; the maximum curvature model is adopted to extract the finger-vein pattern. Then, seven moment invariants are extracted to be matched by the Euclidean distance. In order to solve the problem of high false rate in finger-vein recognition using the single-feature method and the equal weights fusion strategy, the matching scores are fused by the weighted average strategy, and the equal error rate (EER) is minimized to obtain the optimum weights. Finally, the fused matching score is used to make the final decision. Experiment results prove that our algorithm has high performance on recognition rate, which at least reduces 11% in EER compared with the single-feature method and the equal weights fusion strategy.